This paper considers the problem of a robot navi-gating in a crowded or congested environment. Arobot operating in such an environment can get eas-ily blocked by moving humans and other objects. Todeal with this problem it is proposed to attempt to pre-dict the motion trajectory of humans and obstacles.Two kinds of prediction are considered: short-termand long-term. The short-term prediction refers tothe one-step ahead prediction and the long-term tothe prediction of the final destination point of theobstacle’s movement. The robot movement is con-trolled by a Partially Observable Markov DecisionProcess (POMDP). POMDPs are utilized because oftheir ability to model information about the robot’slocation and sensory information in a probabilisticmanner. The solution of a POMDP is computation-ally expensive and thus a hierarchical representationof POMDPs is used.

For mobile robots, navigation in dynamic, real-worldenvironments is a complex and challenging task.Such environments are characterized by their com-plex structure and the movement of humans and ob-jects in them. In such cases, a navigating robot caneasily get blocked by moving humans and obstaclesand may become immobilized and not able to con-tinue its movement towards its goal position untilthe moving objects free its way. To avoid ever get-ting in such a situation, many researchers have triedto predict the motion of humans and obstacles. Fu-ture motion prediction allows the robot to estimateif the way it follows is going to be blocked and thuschange direction before it ever faces this situation.